In this paper we describe a technique for non-keyword

rejection and we will evaluate in the context of an

audiotex service using the ten Spanish digits. The

baseline keyword recognition system is a

speaker-independent continuous density Hidden Markov

Model recognizer. We propose the use of an affine

transformation to the log-probability of the garbage

model, an HMM model trained to account for both nonkeyword

speech and non-stationary telephone noises. The

parameters of the transformation for the case of isolated

keywords are chosen to minimize a cost function that

weighs the keyword error rate, keyword rejection rate

and false acceptance rate according to the a priori

probabilities of keywordhon-keyword and the

requirements of the specific application. This technique

was also extended to embedded keywords (word-spotting).

Use of this rejection technique on the audiotex

application reduced the total cost function up to 20% for

isolated-word case and 12% for the word-spotting case.

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In  Proc. of the International Conference on Acoustics, Speech and Signal Processing

Publisher  Institute of Electrical and Electronics Engineers, Inc.
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